| Issue |
ITM Web Conf.
Volume 77, 2025
2025 International Conference on Education, Management and Information Technology (EMIT 2025)
|
|
|---|---|---|
| Article Number | 01026 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/itmconf/20257701026 | |
| Published online | 02 July 2025 | |
Point-of-interest recommendation based on non-adjacent trajectory interaction model
1 School of Elentronic and Information Engineering, Xi’an Technological University, Xi’an, 710032, China
2 Shaanxi Face-to-Face Information Technology Co., Ltd., Xi'an 712000, China
* Corresponding author: innovator@163.com
Next Point-of-Interest(POl)recommendation aims to predict users'future behaviors based on their historical trajectories, providing significant value toboth users and service providers. Most models fail to capture users'non-adjacent trajectory features, leading to insufficient modeling of users'long-term preferences. Therefore, this paper proposes a Non-Adjacent Trajectory Interaction(NATI) model. The NATI model first uses a multi-dimensional embedding layer to represent user trajectories, then employs multi-head self-attention to capture non-adjacent spatio-temporal features across different subspaces, updating users'long-term preferences.Finally, matching attention is used to match potential locations and predict users'possible POls. Validation on two public datasets demonstrates that the proposed model outperforms baseline models by 8%-16%.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.